d3.2 rvf/malaria study site analysis

HEALTHY FUTURES FP7:266327 – D 3.2 RVF/malaria study site analysis and major findings for RVF & malaria transmission

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HEALTHY FUTURES

Health, environmental change and adaptive capacity; mapping, examining & anticipating future risks of water-related vector-borne

diseases in eastern Africa

Collaborative Project Seventh Framework Programme

Cooperation

Deliverable D3.2

RVF/malaria study site analysis and major

findings for RVF & malaria transmission

Grant Agreement no. 266327

The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/2007-

2013) under grant agreement no 266327. This publication reflects the views only of the author, and the European Union cannot be held

responsible for any use which may be made of the information contained therein.

www.healthyfutures.eu

http://www.healthyfutures.eu/


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Work Package 3

Task 3.1b

Dissemination level Public

Restricted to other programme partners (including the

Commission Service)

Restricted to a group specified by the consortium (including

the Commission Service)

Confidential, only for members of the consortium (including

the Commission Service)

Publishing date Contractual: M24 Actual: M32

Deliverable D3.2 Version Draft Final

WP/Task Leader Mark Booth (UDUR)/Bernard Bett (ILRI)

Contact person Bernard Bett (ILRI)

Contributors Bernard Bett, John Gachohi, Debborah Mbotha

Short summary This deliverable gives provisional results of the on-going analyses on RVF and malaria transmission studies in Kenya. Analyses on RVF are based on historical data on RVF outbreaks recorded in the study site between 1961 and 2007, initial outputs from the RVF dynamical model that is being developed, and data collected from participatory studies. All the analyses on malaria are based on hospital records covering the period 2006 – 2011.

Keywords Malaria, Rift Valley fever, climate, transmission

Document name HEALTHY FUTURES Deliverable 3.2 RVF/malaria study site analysis


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History Chart

Issue Date Changed page (s) Cause of change Implemented by

v.1

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This document may not be copied, reproduced or modified in whole or in part for any

purpose without the written permission from the HEALTHY FUTURES Consortium. In

addition to such written permission to copy, reproduce or modify this document in whole or

part, an acknowledgement of the authors of the document and all applicable portions of the

copyright must be clearly referenced.


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Table of Contents

Table of Contents ...................................................................................................................... iv

List of Tables ............................................................................................................................. vi List of Figures .......................................................................................................................... viii List of Plates ............................................................................................................................ viii List of terms and abbreviations ................................................................................................ ix

Summary .................................................................................................................................... x

1 Ijara district – location and physical features ..................................................................... 2

1.1 Position and size .......................................................................................................... 2

1.2 Administrative and political units ............................................................................... 3

1.3 Ecological zone and topographic features .................................................................. 3

1.4 Climate ........................................................................................................................ 3

1.5 Human population density and settlement patterns ................................................. 4

1.6 Agriculture ................................................................................................................... 5

1.6.1 Wildlife resources and forestry............................................................................ 6

2 Rift Valley Fever .................................................................................................................. 7

2.1 Background .................................................................................................................. 7

2.2 Methodology ............................................................................................................... 8

2.2.1 Analytical framework ........................................................................................... 8

2.2.2 RVFV transmission model .................................................................................... 9

2.2.3 Empirical Studies ................................................................................................ 19

2.3 Results ....................................................................................................................... 26

2.3.1 Analyses of the historical data on RVF outbreaks ............................................. 26

2.3.2 Community-based participatory research survey ............................................. 28

2.3.3 Cross sectional surveys ...................................................................................... 34

2.3.4 Preliminary predictions ...................................................................................... 34

2.4 Discussion .................................................................................................................. 37

3 Malaria .............................................................................................................................. 41

3.1 Background ................................................................................................................ 41


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3.2 Methodology ............................................................................................................. 42

3.3 Results ....................................................................................................................... 43

3.3.1 Descriptive analyses ........................................................................................... 43

3.3.2 Results of statistical analyses ............................................................................. 44

3.4 Discussion .................................................................................................................. 45

4 Way forward on RVF work ................................................................................................ 46

5 References ........................................................................................................................ 48


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List of Tables

Table 2.1. Projected population distributions of Ijara district by age groups for the period:

2008-2012 .................................................................................................................................. 5

Table 2.2. Types of livestock species kept in Ijara and their respective population sizes ......... 6

Table 2.3. Quantities of products generated from livestock in Ijara district in 2008 and their

market values in Kenya shillings ................................................................................................ 6

Table 3.1. Parameters used to initialize the RVFV transmission model .................................. 10

Table 3.2. Parameters used to simulate population dynamics and RVFV transmission in the

vectors ...................................................................................................................................... 15

Table 3.3. Parameters used to simulate livestock population dynamics in the RVFV

transmission model .................................................................................................................. 16

Table 3.4. RVFV transmission parameters in the hosts ........................................................... 17

Table 3.5. Total number of and selected sub-locations by division in Ijara District ................ 21

Table 3.6. A summary of the type of information collected using each of the three PE

techniques during participatory surveys conducted in Ijara District, August-November 2012

.................................................................................................................................................. 23

Table 3.7. Historical outbreaks of RVF involving Ijara and the other districts in the north-

eastern Kenya .......................................................................................................................... 27

Table 3.8. Results of the univariate analyses used to assess the association between RVF

outbreaks in Ijara district and precipitation, temperature and NDVI obtained from ECMWF27

Table 3.9. Random effects logistic regression models evaluating the association between

climate variables (precipitation and temperature) and RVF epizootics in Ijara district, Kenya

.................................................................................................................................................. 28

Table 3.10. Types of livestock species kept and their relative population sizes determined

using median percentage scores (with 10th and 90th percentiles) .......................................... 29

Table 3.11. Median proportions (with 10th and 90th percentiles) representing age category-

specific risks of mortalities in selected livestock species, by season ...................................... 29

Table 3.12. Median percentages (10th and 90th percentiles) of animals sold or slaughtered

in Ijara district by seasons (August 2006 to November 2007) ................................................. 31


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Table 3.13. Reproduction parameters estimated from participatory exercises for the most

important livestock species raised in Ijara district .................................................................. 31

Table 3.14. Monthly normalised vegetation indices (NDVI) for the areas used to graze sheep

and goats by four different grazing communities in Ijara district over the period July 2012 to

July 2012 .................................................................................................................................. 33

Table 3.15. Monthly normalised vegetation indices (NDVI) for the areas used to graze cattle

by four different grazing communities in Ijara district over the period July 2012 to July 2012

.................................................................................................................................................. 33

Table 4.1. Annual statistics on the numbers of insecticide treated nets (ITNs), long lasting

insecticide treated nets (LLINs), arteminisin-combination therapies (ACTs) and the number

of houses covered with indoor residual spraying (IRS) obtained from Ijara district for the

period 2006 - 2007 ................................................................................................................... 44

Table 4.2. Results of statistical analyses conducted to investigate the correlation between

climate variables (rainfall and temperature) and hospital records of malaria cases and the

proportion of positive cases obtained from laboratory analyses (Ijara district, 2006 – 2011)

.................................................................................................................................................. 45


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List of Figures

Figure 2.1. Location of Ijara district in Kenya. ........................................................................... 2

Figure 2.2. Total monthly rainfall and maximum (Tmax) and minimum (Tmin) temperatures

measured at Garissa meteorological station in 2011 ................................................................ 4

Figure 3.1. Flow diagram outlining vectors’ development stages captured in the vector sub-

model ....................................................................................................................................... 12

Figure 3.2. Flow diagram outlining hosts’ infection stages ..................................................... 18

Figure 3.3. Map of Ijara District showing the locations of villages that were surveyed in the

study. The inset is a ma of Kenya showing the location of Ijara district. ................................ 22

Figure 3.4. Predicted population levels of Aedes and Culex mosquitoes over the simulation

period ....................................................................................................................................... 34

Figure 3.5. Predicted RVFV incidence in cattle and sheep over the simulation period; the

inset graph illustrates the patterns of the epidemic at a relatively higher temporal resolution

.................................................................................................................................................. 35

Figure 3.6. Predicted changes in immunity levels following natural exposure to RVFV in

cattle ........................................................................................................................................ 36

Figure 3.7. Expected effect of varying the number of Aedes spp and joint Aedes and Culex

spp breeding sites on RVFV incidence in cattle ....................................................................... 36

Figure 3.8. Predicted RVFV incidence in Ijara, Kenya and Arusha Tanzania ............................ 37

Figure 4.1. Trends in the total number of outpatient malaria cases and those for patients

less than 5 years old attended to in all the health facilities in Ijara district in 2006 to 2011. 43

Figure 4.2. Monthly trends in proportion of cases that are positively diagnosed for malaria

using laboratory tests in Ijara district over the period 2006 – 2011 ....................................... 44

List of Plates

Plate 3.1. A CDC-type light trap set in the bushy Boni Forest near a water body in Ijara

District. ..................................................................................................................................... 26

Plate 3.2. A map indicating migration patterns of livestock in Hara sublocation, Ijara district

developed during one of the participatory rural appraisal meetings ..................................... 32


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List of terms and abbreviations

ACTs Arteminisin combination therapies

AVID Arbovirus Incidence and Diversity project

CBS Central Bureau of Statistics

CDC Centres for Disease Control

ECMWF European Centre for Medium_Range Weather Forecasts

ELISA Enzyme linked immunosorbent assay

FAO Food and Agriculture Organization of the United Nations

GoK Government of Kenya

IBM Individual based model

IDSR Integrated Disease Surveillance and Response

IgG Immunoglobulin G

ILRI International Livestock Research Institute

ITNs Insecticide treated nets

LLINs Long lasting insecticide treated nets

MoH Ministry of Health

NDVI Normalised difference vegetation index

PfRT Parasite rate

RVF Rift Valley fever

RVFV Rift Valley fever virus

SST Sea surface temperatures

Tmax Maximum temperature

Tmin Minimum temperature

TRMM Tropical Rainfall Measuring Mission

WHO World Health Organization


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Summary

The HEALTHY FUTURES project evaluates the effects of environment and climate change

effects on selected vector borne diseases (Rift Valley fever, malaria and schistosomiasis) in

East Africa. The project will use both empirical and simulated data to develop prediction

models and information systems that can support the management of these diseases.

Empirical data will be collected in three pre-determined case study sites including Ijara

district in Kenya (Rift Valley fever and malaria study site), Lake Albert in Uganda

(schistosomiasis study site) and northern and southern Rwanda (a second malaria study

site).

Ijara is one of the six districts in Garissa County, north-eastern Kenya. It falls in V-VI agro-

ecological zones (semi-arid and very arid respectively) with the south-eastern part

neighbouring the coastal strip falling in zone IV (semi-humid to semi-arid zone).

Temperatures range between 15 and 380C though they tend to remain high throughout the

year except in April – August due to the low altitude and semi-arid conditions. Rainfall is low

and bimodal, with its density ranging between 200 mm to 1000 mm per annum. The district

is inhabited by the Somali pastoralists who live in small families commonly in trading centres

or watering points. The average population density is 7 people per square kilometre. The

district was selected for this work because it is a hotspot for RVF and it has also been used

previously by research projects such as Arbovirus Incidence and Diversity (AVID) project for

similar activities; it will be possible, therefore, to obtain secondary data for some of the

analyses. In addition, the district has high levels of poverty, malnutrition, and morbidity

rates especially among children and women.

Since 1961, Ijara has had at least 4 RVF outbreaks – these occurred in the years: 1961, 1962,

1997-98 and 2006-07. These outbreaks produce devastating effects on both public health

and animal production given that the native pastoral communities rely heavily on livestock

for their livelihoods. Preliminary results obtained from an analysis of historical data indicate

that RVF outbreaks are associated with excessive and persistent rainfall that lasts for a

period of at least 3 months. Participatory studies have shown that outbreaks cause

tremendous losses through livestock mortalities, abortions and trade embargoes. An

individual-based model developed as part of the HEALTHY FUTURES project’s activities track

RVF virus transmission dynamics between vectors and hosts and demonstrate that hosts’

herd immunity play a critical role in moderating the frequency of epidemics. The model also

provides a framework for testing alternative scenarios, for example, the effects of varying

relative proportions of livestock and other potential hosts on RVFV transmission. This is

critical for the evaluation of land use and biodiversity changes on the disease incidence and

hence the effectiveness of control measures.


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The level of malaria transmission in the district is low and unstable because of the harsh

climatic conditions. The area is semi-arid with low rainfall density ranging between 200 to

1000 mm per year. Multiple intervention programs have also been implemented in the

district over the last 5 – 10 years. These interventions have had a substantial impact on the

risk of the disease. However, the disease still poses a risk to the local communities

particularly during the wet seasons. Long periods of underexposure, frequent droughts,

cross-border migrations that are common in the area, and use of counterfeit drugs, among

other factors are likely to increase the local community’s vulnerability to the disease

particularly if the on-going intervention programs are temporary halted or discontinued. An

analysis of hospital records obtained from the local health centres did not find any

association between climate variables – precipitation and temperature – probably because

of the effects of the on-going interventions.

More work is being done to generate decision support tools and risk maps for managing

these diseases. This report is intended to give initial findings. It is structured into four

chapters: Chapter 1 describes the location of the district and its physical features; Chapter 2

presents preliminary findings on RVF analyses; Chapter 3 presents results of a statistical

analysis of hospital records on malaria and Chapter 4 outlines some of the work that will be

done in the coming months.


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1 Ijara district – location and physical features

1.1 Position and size

Ijara is one of the six districts in Garissa County, north-eastern Kenya. The district lies

between 10 7`S and 20 3`S and 400 4`E and 410 32`E and borders Fafi district to the north,

Lamu to the south, Tana Delta to the southwest, Tana River to the west and Republic of

Somalia to the east. It covers an area of 9,642 km2 (GoK, 2009). Figure 2.1 shows the

location of the district in Kenya.

Figure ‎1.1. Location of Ijara district in Kenya.


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1.2 Administrative and political units

The district is subdivided into six administrative divisions namely: Masalani, Ijara, Sangailu,

Kotile, Korisa and Bodhai. The district has one constituency (i.e., Ijara), 11 electoral wards

and one local authority, Ijara County Council (GoK, 2009).

1.3 Ecological zone and topographic features

The district falls in V-VI agro-ecological zones (semi-arid and very arid respectively) with the

south-eastern part neighbouring the coastal strip falling in zone IV (semi-humid to semi-arid

zone). Approximately one quarter of the district on the eastern part is covered by the Boni

Forest. The forest is indigenous and constitutes the northern strip of the Zanzibar-

Inhambane coastal forest mosaic. Areas adjacent to the forest fall under the agricultural

Zone IV, which gradually changes to V and VI as one moves westwards. The forest is an

important resource for the local pastoralists since it is used as a dry season grazing site. The

vegetation in the other parts of the district comprises acacia shrubs, star and elephant

grasses, etc. (GoK, 2009).

The district generally has a flat topography interspersed with undulating plains. Its altitude

ranges between 0 and 90 meters above sea level. Most of the district has black cotton and

alluvial soils with small patches of sandy soils towards the coastal border. An analysis

conducted by the GIS Unit, ILRI, indicates that 56% of the district has haplic solonertz soil

type, while 23% and 18% has eutric planosols and eutric vertisols, respectively. These soils

have poor drainage properties and they form deep cracks when dry – they are, therefore,

not suitable for rain-fed agriculture. The Tana River that runs along the western boundary of

the district has a tremendous influence over the climate, settlement patterns, and economic

potential within the district for it forms the single most important source of water. Seasonal

rivers (laghas) that are found in most parts of the district provide water for both human and

livestock consumption during the wet season.

1.4 Climate

Temperatures range between 15 and 380C though they tend to remain high throughout the

year except in April – August due to the low altitude and semi-arid conditions. Rainfall is low

and bimodal and its density ranges between 200 mm to 1000 mm per annum. The two wet

periods in the year occur between March to May and October to December, with the

second period having higher rainfall densities than the former. Rainfall and temperature

patterns for the year 2011 measured at Garissa meteorological station, which represent

most of the north-eastern Kenya including Ijara district, are pre presented in Figure 2.2.


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0

5

10

15

20

25

30

35

40

0

20

40

60

80

100

120

140

160

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecRain

Tmax

Tmin

Rai

nfa

ll (m

m)

Tem

per

atu

re (

°C)

Month

Figure ‎1.2. Total monthly rainfall and maximum (Tmax) and minimum (Tmin) temperatures

measured at Garissa meteorological station in 2011

1.5 Human population density and settlement patterns

The district is inhabited by the Somali pastoralists. They live in small families commonly in

trading centres or watering points. The average population density is 7 people per square

kilometre. The district’s headquarters (Masalani division) has the highest population density

of 13 persons per square kilometre. Factors influencing population distribution are

availability of pasture and watering points for livestock such as dams, wells, boreholes, and

reservoir tanks. Other factors include proximity to schools, health facilities and

administration and police posts as well as district, divisional, locational and sub-locational

headquarters wherein security is assured. These clustered settlement patterns contribute to

overgrazing around watering locations (GoK, 2009).

The housing and population census of 1999 indicated that the district had a human

population of 62,571. This was predicted to be 70,718 in 2008, including 37,136 (52.51%)

males and 33,582 (47.5%) females. At an annual growth rate of 3.5%, this population was

projected to rise to 73,767 people by 2012 (National Coordination Agency for Population

and Development, Ministry of Planning and National Development, 2005). Table 2.1

presents population projection by age groups.


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Table ‎1.1. Projected population distributions of Ijara district by age groups for the period: 2008-2012

1999 2008 2010 2012

M F T M F T M F T M F T

32,890 29,743 62,633 37,136 33,582 70,718 37,980 34,346 72,326 38,737 35,030 73,767

Source: National Coordination Agency for Population and Development, Ministry of Planning

and National Development, (2005).

The projected population figures given in Table 2.1 indicate an increasing trend over the

period. The number of males marginally exceeds the number of females. Ijara division has

the highest population, accounting for 32% of the total. Masalani division, in which the

district headquarters is located, has the second highest population and accounts for 25%.

The district has a population growth rate of 3.5% which is higher than the national average

of 2.9%. Similarly, the district’s mean fertility rate is 7 births per woman while the national

average is 4.9 (Central Bureau of Statistics (CBS), Ministry of Health (MOH), and ORC Macro,

2004). The district’s life expectancy for men is 60 years while that for women is 57 years.

The national average (for both men and women) is 46 years. Current estimate for the crude

death rate is 10 deaths per 1,000. Infant mortality rate averages 91 per 1,000 live births

while that for children under five years of age is 163 per 1,000 births.

Based on these statistics, it was predicted that the population of the district would increase

from 70,718 (level projected for 2008) to 73,767 by 2012. Given that 59% of the district’s

population living in absolute poverty, such an increase in population has negative impacts

on food security, water availability, provision of public health and other social services. Most

of the people depend on livestock. Agriculture (crop production) is another major economic

activity and is largely limited to the Tana basin and Bodhai division.

Overtime, the district has received local immigrants mainly from the Kamba community who

travel in search for blue-collar jobs in the district. It is also probable that citizens of the

Somali Republic who have relatives in the district could have immigrated to the district

when that country was being ruled by insurgents.

1.6 Agriculture

Ijara district has 100,000 ha of arable land of which only 1% is currently under crop

production. Over 90% of land is either trust land or government land that is used by the

local communities for pastoralism. The carrying capacity of the land is 15.5 total livestock

units/ha and the proportion of the population working in the livestock sector is 95%.

However, the potential for crop production is immerse with some isolated farms producing

for the export market. Table 2.2 presents the current livestock species in Ijara and their


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population sizes while Table 2.3 presents quantities of products produced and their market

values.

Table ‎1.2. Types of livestock species kept in Ijara and their respective population sizes

Livestock species Population

Cattle 35, 2617

Sheep 323, 676

Goats 348, 648

Camels 1, 740

Donkeys 8, 096

Pigs 1, 009

Indigenous Chicken 25, 439

Commercial Chicken 3, 279

Bee Apiaries 20

Bee Hives 422

Source: Kenya National Bureau of Statistics, 2009 (http://www.knbs.or.ke/censuspopulation.php)

Table ‎1.3. Quantities of products generated from livestock in Ijara district in 2008 and their market values in Kenya shillings

Product Description Units Value (KSh)

Milk Annual milk production (litres) 13,398,236 401,947,080

Beef Annual beef production (kgs) 3,201 624,000

Mutton Annual mutton production (kgs) 193,390 38,678,000

Goat meat Annual goat meat production (kgs) 209,202 41,840,400

Eggs Annual egg production (trays) 600 180,000

Poultry meat Annual poultry meat production (kgs) 1,000 300,000

Honey Annual mutton production (kgs) 4,260 1,065,000

Source: Kenya National Bureau of Statistics, 2009 (http://www.knbs.or.ke/censuspopulation.php)

1.6.1 Wildlife resources and forestry

Ijara has three national reserves and one community conservancy. Wildlife species present

in the district include the rare Hirola antelope, lions, elephants, buffaloes, monkeys, hippos,

crocodiles, guinea fowls, giraffes, ostriches, leopards, hyenas, warthogs, zebra, cheetahs,

snakes, deer and varieties of birds. There is one non-gazetted forest. Poaching control

measures incorporate routine patrols and participatory wildlife management.

http://www.knbs.or.ke/censuspopulation.php

http://www.knbs.or.ke/censuspopulation.php


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2 Rift Valley Fever

2.1 Background

RVF is a viral zoonosis that mainly affects sheep, goats, cattle and camels. The disease was

first reported in livestock in Kenya around 1915 but it was not until 1931 when the RVF virus

(RVFV) was identified in response to an abortion storm in sheep the Rift Valley, Kenya

following prolonged heavy rainfall (Daubney et al. 1931). Humans become infected after

either a bite of an infected mosquito or by intensive contact with acutely infected animals or

by handling infected tissues. In man, the disease manifests either as a mild influenza-like

syndrome in a majority of cases (> 80 per cent) or as a severe disease with haemorrhagic

fever, encephalitis, or retinitis (Soumare et al., 2012). In Kenya, RVF outbreaks have

previously occurred in 1931, 1951/53, 1961/63, 1967/68, 1977/79, and most recently during

1997/98 and 2006/07 with unusually high human morbidity (between 600 – 700 cases) and

mortality (case fatality rate of 23%) (Anyamba et al., 2009).

The recent outbreak (observed between 4th December 2006 and 21st June 2007) affected a

total of 35 districts in Kenya including Ijara district. The outbreak was preceded by

excessively high rainfall and flooding. A total of 717 human and 8,252 animal cases were

reported though only 216 human and 448 animal cases were confirmed through laboratory

diagnosis. A high percentage (85%) of human cases occurred in four districts namely: Garissa

and Ijara districts in the north-eastern Kenya, Baringo district in the Rift Valley and Kilifi

district in the coast. In Ijara, an extensive serological survey showed that buffaloes,

warthogs and waterbucks had RVFV-neutralizing antibodies, suggesting that these animals

were exposed to the virus during the outbreak (Evans et al., 2008). The district also had RVF

outbreaks in 1961/1962 and 1997/1998 (Woods et al., 2002; Murithi et al., 2011).

A number of analytical studies have been implemented in the district to identify risk factors

for the disease. LaBeaud et al., (2008) determined environmental risk factors and long-term

sequelae of human RVF in Gumarey (village) and Sogan-Godud (urban) areas before the

2006/2007 outbreak. Thirteen per cent of the 248 residents examined were positive for

RVFV antibodies based on immunoglobulin G (IgG) ELISA. This prevalence was higher among

older persons, males, individuals who lived in the village (Gumarey), and those who had

been involved in the disposal aborted foetuses. Seropositive persons were also more likely

to have visual impairment and retinal lesions compared to those that had not been exposed

to the virus. LaBeaud et al. (2011b) further examined 92 randomly selected individuals after

the 2006/2007 outbreak from the same sites. The results showed significant variability in

RVFV exposure in two neighbouring villages that had similar climate, terrain, and vector

density. Individuals that had had a previous exposure (before 2006) had IgG titre

concentration of 1:40 for more than 3 years. Twenty seven out of the 92 newly recruited

individuals (29%, 95% CI: 20%–39%) were seropositive. Factors associated with

seropositivity included living in the rural areas and consumption of raw milk. Entomological


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assessments were also done at the same time; these indicated that Culex spp. constituted

75% of the mosquito vectors trapped and laboratory tests revealed that 22% of the 105

pools tested were positive for RVFV, 18% were positive for West Nile virus while 3% were

positive for both viruses (LaBeaud et al., 2011a).

Hightower et al. (2012) estimated RVF incidence as a function of geological, geographic, and

climatological factors during the 2006/2007 outbreak in the north-eastern Kenya (including

Ijara district), Baringo and Kilifi districts. They established that locations with subtypes of

solonetz, calcisols, solonchaks and planosols soil types, increased rainfall and higher

normalised difference vegetation indices (NDVI) before the outbreak were associated with

increased risk of RVF. It has not been established why the soil types mentioned above are

associated with RVF outbreaks but we hypothesize that their low infiltration rates make

them to be more prone to flooding than other soil types. They might also retain moisture for

an appreciable length of time, therefore allowing infected eggs of floodwater Aedes

mosquitoes to survive for long. Other risk factors that were identified by Hightower et al.

(2012) include low elevation, plains and densely bushed areas. A more recent analysis of

historical data on RVF epizootics corroborate these findings and shows that high and

persistent precipitation over a period of 3 months and low altitude is associated with the

incidence of the disease while the presence of soil sub-types solonetz and luvisols in an area

leads to persistence of outbreaks for a period of at least 3 months (Bett et al., 2012).

Following the 2006/2007 outbreak, ILRI in partnership with the Department of Veterinary

Services (DVS) implemented studies in Garissa and Ijara districts to assess the impact of the

outbreak and identify ways of improving the prediction, detection, and response to RVF

(ILRI, 2007). The study found out that the severity of the epidemic particularly in the north-

eastern Kenya was exasperated by delays in recognizing risk factors and in taking decisions

to prevent and control the disease. The study found out that epidemics of RVF can most

effectively be prevented and controlled through the active monitoring of key risk factors

leading to timely decision making and the targeting of prevention and control resources.

The new transmission studies being done under the HEALTHY FUTURES project build on the

work that has been done to further investigate the disease transmission dynamics. They

utilize a mechanistic model that simulates the disease transmission dynamics as an

analytical framework which specifies the type of data or information required for a holistic

assessment of the disease system.

2.2 Methodology 2.2.1 Analytical framework

RVFV transmission mechanisms are poorly understood partly they involve complex

interactions between multiple agents (a wide range of vector and host species) and drivers

that operate at local (e.g. socio-economic practices and land use) and regional levels


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(including climate change). In the Horn of Africa, RVF epidemics occur periodically following

periods of prolonged heavy rainfall. It is believed that the virus persists during the inter-

epidemic periods in drought resistant floodwater Aedes eggs. It is also thought that riverine

vegetation, moist bushed and wooded grasslands and forests can support endemic

transmission of the virus probably because these areas always have high population

densities of mosquito vectors and potential reservoir hosts.

This study utilizes an individual based RVF model (IBM) as a framework for studying these

transmission dynamics. The study area, as described above, is inhabited by transhumant

pastoralists whose movements (to and from wet and dry grazing areas) could be important

for RVFV maintenance and transmission. Individual based models (IBMs) are suitable for

studying such complex non-linear systems where space is crucial and agents’ positions are

not fixed. They are also useful for simulating agents’ behaviours especially if they are

expected to change over time as they adapt based on acquired knowledge or in response to

new challenges.

The model is currently being used to determine types of studies that should be

implemented to obtain input parameters. Scenario analyses are also being implemented to

generate hypotheses on RVFV transmission mechanisms. The structure of the model is

described below.

2.2.2 RVFV transmission model

The key components of the model include: (i) the environment or landscape, (ii) agents, and

(iii) processes describing interactions in time and space.

2.2.2.1 The environment

The model simulates livestock and vector population dynamics and RVF transmission in a

spatially-explicit environment that is subdivided into 100 x 100 grids of square cells

measuring 500 x 500 m. This framework allows for the incorporation of spatial

heterogeneities in the model such as the locations of the grazing sites by season and vector

breeding sites. A reliable estimate of the carrying capacity of the area has not been

obtained. For the purposes of this analysis, it is assumed that the current cattle and sheep

populations of 300,000 and 600,000, respectively, (Department of Veterinary Services,

unpublished data) represent equilibrium populations of these livestock species. Sensitivity

analyses are, however, being conducted to determine the effects of varying the equilibrium

population sizes on epidemic patterns.

Vector breeding sites (dambos) are randomly distributed within the grid. The number used

at the model initialization stage is given in Table 3.1.


10

2.2.2.2 Agents

Two host species, namely cattle and sheep are used as agents in the model. Their attributes

include species (cattle or sheep), age (neonate, weaner, yearling or adult), sex (male or

female), infection status (susceptible, exposed, infectious or removed/resistant), time since

infection and physical location. Animals are also aggregated to form herds or flocks. The

numbers of herds, flocks and individuals generated at the model initialization stage are

given in Table 3.1.

2.2.2.3 Dynamic processes

Dynamic processes that drive the model operations are classified into three, these are:

i. Mosquito population dynamics,

ii. Host population and movement dynamics, and

iii. RVFV transmission dynamics.

All of these processes are updated on daily basis.

Table ‎2.1. Parameters used to initialize the RVFV transmission model

Model component Value Description a

Host Cattle 100 Number of herds created in the model

30 Number of cattle randomly assigned to each herd; their ages in days are randomly allocated from 1 to 3,650 days (10 years)

0.3 Probability of being male

Sheep 100 Number of flocks created in the model

60 Number of sheep randomly assigned to each flock; their ages in days are randomly allocated from 1 to 1,825 days (5 years)

0.4 Probability of being male

Vector breeding sites (dambos)

Aedes spp 250 Number of Aedes spp breeding sites

Culex spp 1000 Number of Culex spp breeding sites

Vectors Aedes spp 2500 Number of RVFV uninfected eggs

250 Number of RVFV infected eggs

Culex spp 100 Number of eggs a These parameters are subjective; however, sensitivity analyses are being conducted to gauge their impacts on epidemic patterns

Mosquito population dynamics

The model considers two RVFV vectors, namely Aedes mcintoshi (indicated throughout this

report as Aedes spp.), as the primary vector, and Culex spp., to represent all the possible

secondary vectors. Their population dynamics are simulated using a stage-structured

transition matrix model described by Yussof et al. (2012) based on the parameters used are

presented in Table 3.2. This model illustrated in Figure 3.1. Each vector has four life stages,

i.e., eggs, larvae, pupae and adult. Each stage has corresponding probability of surviving and


11

staying in stage i, denoted by iP , and the probability of surviving and growing from stage i to

stage i+1, denoted by iG .

A list of these development and survival probabilities constitute a transition matrix A and

the population of a given stage at time t, X(t), is obtained by multiplying the transition

matrix with X(t-1), the population of each life stage at time t-1.

iP and iG were computed as described by Yussof et al. (2012) based on iS , the survival rate

for stage i, and id the duration in that stage i, as follows:

i

i

d

i

i

d

ii

S

SSG

1

)1( (Eq. 1)

and i

i

d

i

d

iii

S

SSP

1

)1( (Eq 2)

Climate variables: temperature, precipitation and humidity influence the development rates

of most vectors including mosquitoes. At the moment though, only daily rainfall densities

obtained from Tropical Rainfall Measuring Mission (TRMM) are used to estimate the

development and survival probabilities. Work is underway to include temperature estimates

to these functions.

For Aedes spp., simulation starts with the hatching of eggs in inundated soils. During dry

periods, eggs of Aedes spp that are dormant in dried up soils are assumed to suffer a low

baseline mortality rate of μAe. When conditions that favour hatching are provided (i.e.,

flooding that persists for at least 2-3 days), hatching occurs at the rate, HA. Hatching rate is

made to depend on the amount of flooding, therefore extensive floods leads to the hatching

of a higher proportion of dormant eggs.

Larvae develop into pupae after PA days while pupae emerge as adults after EA days. Larvae,

pupae and adults have baseline mortality rates of μAl, μAp and μAa, respectively. Females seek

a blood meal every GA days. Following a successful feeding, these mosquitoes lay eggs on

moist soil at the edge of the flooded areas. Aedes spp are assumed to lay SA eggs per batch;

all the eggs laid by infected Aedes spp are assumed infected trans-ovarially. RVFV is thought

to be transmitted transovarially by floodwater Aedes mosquitoes (EFSA, 2005). Mosquitoes

that emerge from the infected eggs develop into infectious vectors. It is assumed the

development rates of the immatures, and feeding frequencies and baseline mortality rates

for the mature stages are not influenced by RVFV infection. In addition, the model does not

as yet allow for the variation in the duration of the gonotrophic cycle, or the number of eggs

laid per batch, with an increase in the age of the vector.


12

Culex spp lay eggs on fresh or existing pools of water; these eggs cannot withstand

desiccation, therefore they don’t remain dormant in the soils like those of the Aedes

mcinthoshi. The development processes from eggs to adults are similar to those described

for Aedes spp. There is however no transovarial transmission of RVFV in Culex spp or in any

other secondary vectors.

Persistent rainfall and flooding provide extensive breeding surfaces especially for Culex

mosquitoes. Linthicum et al. (1983) indicates that flooding that persist for at least 4-6 weeks

allows for the development of massive swarms of secondary mosquitoes which amplify the

transmission of RVF when cattle, goats and sheep are present. Similarly, a participatory

survey that was carried out in Ijara in following the 2006-2007 RVF outbreak established

that the mean interval in days between the start of heavy rains and appearance of mosquito

swarms was 23.6 days (Jost et al., 2010). To mimic these dynamics, the number of Culex

mosquitoes obtained from the matrix model is amplified based on a by 23-day cumulative

rainfall. The cumulative rainfall is also used to control the hatching of infected Aedes spp.

eggs that remained dormant in the soils during the dry and low rainfall periods.

Model runs generated for this analysis focussed on the period January 1, 2005 to July 23,

2010 so as to capture the recent RVF outbreak that occurred in the district between October

2006 and February 2007.

Figure ‎2.1. Flow diagram outlining vectors’ development stages captured in the vector sub-model


13

2.2.2.4 Host population and movement dynamics

The number of herds and flocks used to initialize the model are given in Table 3.1. These

populations represent 1% of the assumed equilibrium population of cattle and goats in the

area.

A new host is allowed to enter the system through births or purchase while exits occur

through mortality, RVFV-associated mortality (case fatality) or through sale; the parameters

used to run these simulations are given in Table 3.3. Mature females breed for the first time

at ages BRC and BRS for cattle and sheep, respectively. The probability of a female giving

birth at the end of the gestation period (taken to be 9 months for cattle and 5 months for

sheep) depends on

i. conception probability: CeS in sheep and CeC in cattle

ii. abortion probability, classified into baseline abortion probability (AbC in cattle and

AbS in sheep) and RVF-associated abortion only for RVFV infected animals (AbCRVF

and AbSRVF, respectively).

After parturition, cattle and sheep will undergo a waiting period of 180 and 60 days

respectively before it can start breeding again.

Hosts move between wet and dry grazing sites depending on season. In the current model,

host movements are driven by cumulative daily rainfall. Livestock are confined to the wet

season grazing areas when the cumulative (TRMM) rainfall over a period of 21 days is >100

mm/month. Below this threshold, livestock are transferred to a dry season grazing area.

Movement ranges within each site are outlined in Table 3.3.

2.2.2.5 RVF virus transmission dynamics

The probability that a given host gets exposed to RVFV depends on its level of interaction

with infectious vectors present in the area. Given that a host can get infection either from

either of the vectors used in the model (Aedes spp and/or Culex spp), the model simulates

infection processes for each vector independently and then aggregates them to obtain a

composite transmission coefficient, hi for each host. Parameters that are multiplied to

obtain the transmission coefficient for a given vector include:

(i) The ratio of the population of the vector species to that of a specified host species,

(ii) The vector’s biting rate,

(iii) The probability that the vector feeds on the host depending on the blood meal

index,

(iv) The probability that the host gets infected following a bite by an infectious

mosquito;


14

(v) The prevalence of RVFV infection in the vector,

The composite transmission coefficient ( hi ) is transformed into host’s infection probability

( hip ) using the formula:

)exp(1 hihip (Eq. 4)

A host’s status changes to exposed following exposure to RVFV infection. It remains in this

state for LC (cattle) or LS (sheep) days after which it becomes infectious. The infectious state

lasts for iC (cattle) or iS (sheep) after which a host moves to immuned/removed state. It is

assumed that following recovery, a host remains immuned for the remainder of its life.

Table 3.3 outlines the parameters used to simulate RVFV transmission in the host while

Figure 3.2 represents host’s infection stages. Where possible, the model picks an input

parameter for simulating state transitions from a continuous uniform distribution bounded

by minimum and maximum values.

A compartmental SIR model is used to simulate RVFV infections in mosquitoes. Susceptible

vectors can pick RVFV infection either from infectious cattle or sheep. The transmission

coefficient for vector i is estimated by first simulating the interactions between that vector

and host species i, followed by aggregating the estimates for all the species that each vector

would feed on. Parameters used to estimate this coefficient include:

i. vector biting rates

ii. blood meal index (indicating the proportion of meals obtained by vector i on host j.

iii. the probability that the vector will get infected from an infected blood meal

iv. the prevalence of RVFV infection in host i.

Following exposure, susceptible mosquitoes will join exposed category for LAe days (Aedes

spp) or LCu days (Culex sp). They will become infectious at the end of that period. It is

assumed that an infectious vector remains at this state for its remaining lifetime. Most of

the input parameters for this work have been obtained from the literature.


15

Table ‎2.2. Parameters used to simulate population dynamics and RVFV transmission in the vectors

Parameter Symbol Value and units

Comment Source

Aedes spp

Average length of time the mosquito can live (lifespan) AA 14-30 days Estimate for Aedes aegypti Gaff et al., 2007

Gonotrophic cycle GA 3 days Neto and Navarro-Silva, 2004; Pant and Yasuno, 1973

Number of eggs laid per batch SA 63 Estimate for Aedes aegypti Otero et al., 2006

Egg development rate HA 0.25 - 0.33 Estimate for Ae. vexans arabiensis Ndiaye et al., 2006; Clements and Paterson, 1981

Larvae development rate PA 0.18 Rueda et al., 1990

Pupae development rate EA 0.92 Rueda et al., 1990

Proportion of blood meals from cattle aAc 0.0001 Subjective estimate Not available in literature but values that mimic expected incidence levels in respective hosts are used

Proportion of blood meals from sheep aAs 0.0001 Subjective estimate Not available in literature but values that mimic expected incidence levels in respective hosts are used

Egg mortality probability μAe 9.1*10-5 Subjective estimate Not available in literature but low estimate used to ensure persistence over time

Larvae mortality probability μAl 0.2 For Aedes aegypti; temperature dependent

Yusoff et al, 2012; Otero et al, 2006

Pupae mortality probability μAp 0.1 For Aedes aegypti; temperature dependent

Yusoff et al, 2012; Otero et al, 2006

Adult mortality probability μAa 0.1 Estimate for Aedes aegypti McDonald, 1977

Probability of infection following ingestion of infected blood meal in susceptible mosquitoes

bAe 0.38 – 0.86 Estimate for Aedes notoscriptus Turell and Kay, 1998

Culex spp

Average length of time the mosquito can live AC 21 - 30 days http://www.mosquitoes.org/downloads/life-cycle.pdf (Accessed 5/11/12)

Gonotrophic cycle GC 3 days Elizondo-Quiroga et al., 2006

No of eggs laid per bacth SC 240 Estimate for Culex pipiens http://www.mosquitoes.org/downloads/life-cycle.pdf (Accesed on 5/11/12), http://www.metapathogen.com/mosquito/culex/ (Accessed 19/12/12)

Larvae development rate PC 0.1 Rueda et al., 1990

Pupae development rate EC 0.2 Rueda et al., 1990

Proportion of blood meals from cattle aCc 0.25 Subjective estimate No estimate found in literature; Culex spp are nocturnal and so would access livestock in the sheds in the night

Proportion of blood meals from sheep aCs 0.25 Subjective estimate

Egg mortality probability μCe 0.1

Larvae mortality probability μCl 0.2 Rueda et al., 1990

Pupa mortality probability μCp 0.1 Rueda et al., 1990

Adult mortality probability μCa 0.1 Estimate for Culex pipiens Jones et al., 2012; Reisen et al., 1991

Probability of infection following ingestion of infected blood meal bCu 0.3 - 0.89 Estimates for Culex annulirostris, Culex zombaensis

Turell and Kay, 1998

http://www.mosquitoes.org/downloads/life-cycle.pdf%20(Accessed%205/11/12)

http://www.mosquitoes.org/downloads/life-cycle.pdf%20(Accesed%20on%205/11/12),%20http:/www.metapathogen.com/mosquito/culex/%20(Accessed%2019/12/12)

http://www.mosquitoes.org/downloads/life-cycle.pdf%20(Accesed%20on%205/11/12),%20http:/www.metapathogen.com/mosquito/culex/%20(Accessed%2019/12/12)


16

Table ‎2.3. Parameters used to simulate livestock population dynamics in the RVFV transmission model

Parameter Symbol Value and unit Comments Source

Maximum population of livestock that Ijara can sustain indefinitely (Carrying capacity) K 15.5 TLU/ha Ijara district development plan, 2008-2012

Cattle: movement ranges and demographic parameters

Daily movement range in wet/dry season MoveC 11/13 km Ijara district development plan, 2008-2012

Average length of time a bull/cow is retained in a herd AC 1-12/7-12 years Ijara participatory survey, 2012

Proportion of cattle that successfully conceive after a natural service CeC 0.8 Median estimate Ijara participatory survey, 2012

Proportion of abortions that are expected to occur over a gestation period (baseline) AbC 0.176 Median value for wet and dry seasons, respectively

Jost et al 2010; Ijara participatory survey, 2012

Proportion of animals that abort due to RVFV infection during an outbreak period AbCRVF 0.471 Jost et al., 2010

Probability of an animal being introduced into a herd EnC 0.003 Ijara participatory survey, 2012

Daily baseline mortality rate (bull/cow) mC 2.3*10-3 to 2.7*10-3

2.3 *10-4 to 3.9*10-4 Estimated as an inverse of the lifespan Ijara participatory survey, 2012

Probability that an animal is removed from a herd in the wet season ClCwet 1.4*10-4 to 1.4*10-3 Estimates include animals sold or slaughtered Ijara participatory survey, 2012

Probability that an animal is removed from a herd in the dry season ClCdry 3.3*10-4 to 1.5*10-3 Estimates include animals sold or slaughtered Ijara participatory survey, 2012

Sheep: movement ranges and demographic parameters

Daily movement range in wet/dry seasons MoveS 4.5km Ijara participatory survey, 2012

Average duration that a male/female is retained in a flock AS 1-3/4-8 years Ijara participatory survey, 2012

Proportion of sheep that successfully conceive after a natural service CeS 0.9 Ijara participatory survey, 2012

Proportion of abortions that are expected to occur over a gestation period AbS 0.2 Medians for wet and dry seasons Ijara participatory survey, 2012

Proportion of animals that abort due to RVFV infection during an outbreak period AbSRVF 0.9 Ijara participatory survey, 2012

Probability of an animal being introduced into a herd EnS 0 - 0088 Ijara participatory survey, 2012

Baseline mortality (ram/ewe) mS 4.6*10-3 to 2.7*10-3 3.4*10-4 to 6.8*10-4

Estimated as an inverse of the lifespan Ijara participatory survey, 2012

Probability that an animal is removed from a herd in the wet season ClSwet 3.3*10-4 to 2.2*10-3

Estimates include animals sold or slaughtered Ijara participatory survey, 2012

Probability that an animal is removed from a herd in the dry season ClSdry 3.7*10-4 to 3.3*10-3

Estimates include animals sold or slaughtered Ijara participatory survey, 2012


17

Table ‎2.4. RVFV transmission parameters in the hosts

Parameter Symbol Value and unit Comments Source

Cattle

Probability of infection in neonates following a bite from an infected mosquito bNC 0.9 Subjective estimate

Gestimate; sensitivity analysis to be done

Probability of infection in weaners following a bite from an infected mosquito bWC 0.7 Subjective estimate


Probability of infection in yearlings following a bite from an infected mosquito bYC 0.6 Subjective estimate


Probability of infection in adults following a bite from an infected mosquito bAC 0.5 Subjective estimate


Latent period Interval between exposure to RVFV to occurrence of clinical signs LC 1-6 days Niu et al., 2012; Gaff et al., 2007

Infectious period - time period over which infected cattle can transmit RVFV iC 2-7 days Pepin et al., 2010; McIntosh et al., 1973

Case fatality-neonates mNCRVF 0.1 - 0.7 Gaff et al., 2007; Burnham and Musser, 2006

Case fatality rates - weaners mWCRVF 0.1 – 0.7 Gaff et al., 2007; Burnham and Musser, 2006

Case fatality rates - yearlings mYCRVF 0.05 Gaff et al., 2007; Burnham and Musser, 2006

Case fatality rates - adults mACRVF 0.05 Gaff et al., 2007; Burnham and Musser, 2006

Immunity period- time period when an animal is resistant to RVFV infection rC - Life long

Sheep

Probability of infection in neonates following a bite from an infected mosquito bNeonS 0.9 Subjective estimate


Probability of infection in weaners following a bite from an infected mosquito bWeanS 0.9 Subjective estimate


Probability of infection in yearlings following a bite from an infected mosquito bYearS 0.8 Subjective estimate


Probability of infection in adults following a bite from an infected mosquito bAdultS 0.7 Subjective estimate


Latent period - Interval between exposure to RVFV to occurrence of clinical signs LS 1-6 days Niu et al., 2012; Gaff et al., 2007

Infectious period - time period over which infected sheep can transmit RVFV iS 2-7 days Pepin et al., 2010; McIntosh et al., 1973

Case fatality rates-neonates mNSRVF 0.9 Gaff et al., 2007; Burnham and Musser, 2006

Case fatality rates- weaners mWSRVF 0.8 Gaff et al., 2007; Burnham and Musser, 2006

Case fatality rates- yearlings mYSRVF 0.3 Gaff et al., 2007; Burnham and Musser, 2006

Case fatality rates- adults mASRVF 0.1 Gaff et al., 2007; Burnham and Musser, 2006

Immunity period- time period when an animal is resistant to RVFV infection rS - Life long


18

Figure ‎2.2. Flow diagram outlining hosts’ infection stages

2.2.2.6 Model validation

Given the multiple and complex interactions that the model is structured to simulate; it will

not be possible to use traditional methods of validation, e.g. fitting the model empirical

data. In addition, there is scanty data on temporal-spatial distribution of RVF incidence –

most of the available records have been collected during epidemics. Attempts have been

made, therefore, to test the model using pattern oriented modelling approaches. This is an

attempt to establish whether the model mimics RVF occurrences at different scales and

ecologies other than that used to build the model. In this analysis, Arusha region of

Tanzania that officially reported the 2007 RVF outbreak in February 12, 2007 was used. Daily

TRMM rainfall data for the area were obtained and used to drive the model. Temporal

patterns of the RVF outbreak were then analysed against those observed in Ijara.

In future, model validation will include testing various parameterizations of the input

parameter values to determine how well they simulate observed patterns.


19

2.2.3 Empirical Studies Three main studies have been done to generate additional information for RVFV modelling;

these include:

1. Statistical analyses of historical data on RVF outbreaks in Ijara district to determine

the correlation between climate variables (temperature and rainfall) and the

outbreaks,

2. Participatory epidemiological surveys to determine types of livestock species kept

and their proportions, livestock demographic parameters, and movement patterns,

3. Entomological and epidemiological surveys to determine the risk of RVFV infection in

livestock

2.2.3.1 Analysis of historical data on RVF outbreaks

Annual records on RVF epizootics in Kenya dating back to 1979 were obtained from CDC

Kenya. RVF epizootics included outbreaks associated with stormy abortions in livestock

especially small ruminants and hemorrhagic syndrome that occurred after prolonged

periods of heavy rainfall, and were confirmed using laboratory tests [ELISA] or reverse

transcriptase polymerase chain reaction [PCR]). Outbreaks were recorded by year, province,

district and area. For the purpose of this analysis, the data were restructured by: (i)

classifying the areas affected by divisions defined during the 1999 human population census

(n = 505), and (ii) refining the time component of the outbreaks from an annual to monthly

time scale. The refinements were made with reference to records kept at the Department of

Veterinary Services.

Gridded climate data comprising monthly mean precipitation, maximum and minimum

temperatures for the period January 1979 to December 2010 were obtained from the

European Centre for Medium-Range Weather Forecasts (ECMWF). The data merged with

the disease data and kept in a database designed using MS Access database. They were

subsequently exported to STATA/SE 11.1 for statistical analysis. A division was used as the

unit of analysis. The outcome ( ijy ) represented the infection status (Yes/No) of a division in

a given month, therefore it was analyzed as a dichotomous outcome with a binomial

distribution, i.e., ),1( ijijij Binomialy .

Univariate logistic regression models were used to assess the association between the

climate variables: precipitation and temperature and RVF outbreaks. Alternative forms of

the climate variables were tested; precipitation, for instance, had 7 alternative formulations

including:


20

- monthly values,

- lagged values by 1 and 2 months,

- running cumulative values for the recent 2 and 3 months,

- running mean values for the recent 2 and 3 months.

Maximum and minimum temperatures and NDVI values were used in the analyses as well

and competing models compared based on the log likelihood values.

The choice of the variables and the lags tested was based on the studies that have been

done by Anyamba et al. (2009) which indicates that rainfall, NDVI, sea surface temperatures

and outgoing long wave radiation are good predictors for RVF, with NDVI representing

ecological variables. Anyamba et al. (2012) also suggested that cumulative rainfall anomaly

for 3-4 months immediately preceding an outbreak is critical for RVF outbreaks in East

Africa. Temperature does not change much in the area, so lagging was not considered for

this variable.

Random effects logistic regression models were also fitted to the data to account for

clustering of observations in time (due to repeated observations by division). One model

had precipitation (3-month aggregate) and minimum temperature as predictors while the

other had precipitation as the only predictor. In both models, division was treated as a

random effects variable and the correlation structure (for observations within a division)

were assumed to be unstructured. The structure of the model used was as follows:

jijij

jijij

jijijuxx

uxyp

uxyp

210

),0(

),1(ln (Eq. 5)

With ),0( 2Nu j ; i = 1 …1185; j = 1…6.

Given that the analyses presented in this report were limited to the data from Ijara district,

it was not possible to include the other potential predictor variables such as elevation, soil

types, land use given that most of their values would be similar.

2.2.3.2 Community-based participatory research survey

Identification and mapping of the survey sites

Participatory surveys were held between August and November 2012 to collect information

on livestock demographics and movement patterns. A sub-location, the smallest

administrative area with a human population of 4,000 – 5,000 was used as the sampling

unit. A total of 27 units were selected using stratified random sampling technique from a

sampling frame that comprised 40 sub-locations. A division was used as a stratifying


21

variable; the total number of sub-locations per division and the number that were selected

are outlined in Table 3.5.

Table ‎2.5. Total number of and selected sub-locations by division in Ijara District

Division Total number of sub-locations Number of selected sub-locations

Sangailu 8 4

Ijara 10 7

Masalani 13 11

Korisa 2 1

Kotile 5 3

Bodhai 2 1

One site within a sub-location was purposefully selected for an interview. A site was

selected if it had a majority of the families clustered in a small area. Each meeting

comprised at least 10 participants and it involved the local pastoralists and community

leaders. These meetings were convened with the help of the community animal health

workers and the local administrator, which in most cases was the Chief of the area. The

meeting sites were geo-referenced after the interview using the Arc 1960 Geographic

Coordinate System. Figure 3.3 shows the distribution of these sites within the district.

Data collection checklist

Semi-structured interviews were carried out using the local Somali language with the help of

a translator -- each session took about 1 hour. The interviews were guided by a checklist of

open-ended questions. Probing was also done to investigate other relevant issues that

emerged from these discussions. The main items that the checklist covered include:

1. Livestock (cattle, sheep and goats) population dynamics:

a. Types of livestock species kept and their relative population proportions

b. Age at first breeding, by season

c. Interval between parturition and subsequent heat, by season

d. Frequency of repeat breeding

e. Frequency of twinning

f. Frequency of abortions, by season

g. Classification of age categories and identification of age ranges in each

category

h. Maximum age attained in both sexes (lifespan)

i. Expected mortality for each age category, by season

j. Frequency of sales and slaughter, by season


22

2. Movement patterns – using participatory mapping and timelines to determine

livestock movement patterns

Figure ‎2.3. Map of Ijara District showing the locations of villages that were surveyed in the study. The inset is a ma of Kenya showing the location of Ijara district.

Participatory epidemiological techniques

Participatory epidemiological (PE) techniques used in the surveys include semi-structured

interviews, proportional piling and participatory mapping. These techniques have been

described by Cleaveland et al. (2001), Catley and Mariner (2002) and used in several studies

including Bedelian et al. (2007) and Bett et al. (2009). Table 3.6 outlines the specific

information gathered using each of these methods.


23

Table ‎2.6. A summary of the type of information collected using each of the three PE techniques during participatory surveys conducted in Ijara District, August-November 2012

Participatory technique Information gathered

Semi-structured interviews Types of livestock species kept per sub-location

Age at first breeding

Interval between parturition and subsequent heat

Frequency of repeat breeding

Frequency of twinning

Classification of age categories

Determining the age ranges in each category

Lifespan, by sex

Proportional piling Relative abundance of livestock species

Proportion of pregnancies carried to term (% of abortions)

Mortality and case fatalities by age group and season

Proportion of animals sold and slaughtered by season

Participatory mapping Location of settlements and seasonal grazing sites

Timeline Livestock movement patterns between July 2011 and July

2012

Proportional piling

Proportional piling is a scoring technique used to determine perceptions on the relative

importance, abundance or frequency of a list of items. It uses a set of counters (e.g. beans,

pebbles, etc.) that are piled against a given item and then counted to determine relative

percentages or proportions. This survey used a total of 100 beans for all the exercises

conducted.

To determine the relative proportions of livestock species kept, participants were first asked

to list the type of livestock species commonly kept in their area. The responses given (e.g.,

cattle, sheep, goats, chickens) were listed on a flip chart. The participants were then given

100 beans to distribute to the listed items (species) based on the relative abundance of the

livestock species assuming that 100 beans represented the population of livestock in the

area. Circles were often drawn besides each item to guide the participants on where to

place a pile of counters for a species. Livestock species that had the highest population got a

bigger pile of beans and vice versa. The piles were counted when all the participants had

settled on the distribution provided. They were also asked to give reasons that supported

the results observed – e.g. why a particular species was perceived as having the

highest/lowest population sizes.


24

The same approach was used for the other proportional piling exercises. For example, on

the proportion of abortions/pregnancies carried to term, participants were given 100 beans

to represent animals that were pregnant. They were asked to divide the beans into two: the

number of animals that was expected to carry their pregnancies to term verses the number

that would abort. The exercise was completed for a peacetime period (no major disease

outbreaks) and for periods with RVF epidemics. Other exercises involved determining the

relative population sizes of age cohorts of cattle, sheep and goats (neonates, weaners,

yearlings and adults) identified by the participants, and the relative proportion of animals

lost through mortality, sold, or purchased by season.

Data obtained from these exercises were entered into a database designed using MS Excel

and analysed in SATA 11 using non-parametric statistical tests. Medians and their respective

10th and 90th percentile ranges were estimated from the proportional piling scores.

Participatory mapping and timelines

Participants were guided to develop maps of their areas indicating human settlements,

grazing sites, watering points, roads and service centres e.g. towns. These maps were used

to facilitate discussions on a variety of socio-economic activities including livestock grazing

patterns. Timelines were used together with the maps to identify locations where livestock

were, on a monthly basis, over the period July 2011 to July 2012. Timelines on livestock

movements/locations were developed in a reverse order starting with identification of the

sites where livestock were in July 2012, and the earliest time (month) when these animals

were taken there. This approach was repeated until the full period specified above was

covered. Mapping of the livestock movement patterns was done by species (specifically

cattle, sheep and goats).

Data on livestock movement patterns obtained from the participatory mapping exercises

were entered into a database designed using MS Excel. The data variables that could be

formulated include: sub-location, GPS coordinates of the interview sites and other locations

that had been used for grazing over the year, livestock species, month/year, and an

indicator variable which when used together with the month/year specifies whether a given

livestock species was just arriving at a given grazing site, had been there for some time or

was being moved out to other sites with more pasture/water. Monthly mean NDVI data for

all the geo-referenced sites for the study period were obtained from SPOT VEGETATION,

filtered and merged with the movement data obtained from the map. Statistical analyses

were done to determine mean NDVI values for periods when livestock were being moved

out of their recent grazing sites. Up to 1000 bootstrap samples were generated from the

sample and used to estimate 95% confidence intervals for the mean NDVI values for each

site at the time when animals were being moved out from these areas. These analyses were

done in STATA 11 and the results represented thresholds for livestock movement from


25

specific sites. Movement patterns for sheep and goats were combined since these livestock

species were often moved to similar locations.

2.2.3.3 Cross-sectional surveys to determine the risk of RVFV infection in livestock

Cross sectional surveys were implemented in the district between October and November

2012 at a time when the government had issued a warning on the likelihood of an RVF

outbreak. The Meteorological Department had predicted higher than normal amounts of

rainfall for the period. Flooding was expected to occur and contingency measures for RVF

were being put in place.

Eleven sites (10 sites in homesteads area and 1 site in the Boni forest) were selected and

used for the survey based on historical information suggesting that these areas had been

involved in the 2006/2007 outbreak. In the homesteads, vectors were sampled using CDC

miniature light traps placed in the livestock night sheds. This trapping targeted night-time

host-seeking mosquitoes. Three traps were set each evening (6 pm) and left overnight and

gathered the following morning. Trapping in the Boni forest targeted day-time host-seeking

mosquitoes (Plate 3.1). The forest is considered to be a good breeding site for mosquitoes

due to high humidity, dense vegetation, presence of hosts for blood meal and presence of

water bodies. Samples were barcoded (by trap) and transported alive to a field laboratory

where they were sorted, identified to genus level and frozen for storage and transportation.

Pooling was done by genus, traps and trapping sites and transported to ILRI Nairobi where

they will be subjected to further laboratory analyses to identify blood meal sources,

infection status and species diversity.

In the same sites, 300 blood samples were collected from cattle and sheep. Herds/flocks

sampled included: (i) animals that had just been brought back from the Boni forest (given

that the short rainy season was commencing and the pastoralists were bringing their

animals back home), and (ii) herds/flocks had not been vaccinated. The sample size (n = 300)

used represented minimum number of livestock that would be needed to detect RVFV

infection. This number was distributed It was estimated using the formula:

))}2

1((()1{( /1

d

Nn d (Eq. 6)

where N - the population size (900,000); α - 1 - confidence level (0.05); d - the estimated

minimum number of diseased animals in the district (population size × the minimum

expected prevalence (1%)). This estimation mainly targeted small ruminants (sheep and

goats in equal proportions) because they are not usually vaccinated against RVF compared

to cattle and the commonly used serological tests do not have the capacity to differentiate

infection from vaccination. Cattle, however, can act as good sentinels for RVFV infection


26

because they travel much further than the small ruminants and so they were likely to get

greater exposure to mosquito-borne viruses. Limited attention was focussed on cattle in this

study because there was no assurance that the project team will get unvaccinated herds.

Community animal health workers were used to identify appropriate herds/flocks to

sample. For each animal recruited, 20 ml venous blood was drawn from the jugular vein

using heparinized vacutainer tubes and transported to the field laboratory where each

sample was aliquoted into 5 ml barcoded vials. The samples were then frozen and

transported to ILRI Nairobi for further laboratory analyses.

Plate ‎2.1. A CDC-type light trap set in the bushy Boni Forest near a water body in Ijara District.

2.3 Results

2.3.1 Analyses of the historical data on RVF outbreaks

Ijara district, like those affected by RVF outbreaks in the north-eastern Kenya, has recorded

at least 4 outbreaks since 1961 (Table 3.7). Before then, outbreaks were confined to a few

districts in the Rift Valley. The data given in the table suggest that all the districts reported

outbreaks at the same time.

Table 3.8 gives results of univariate analyses that were done to evaluate unconditional

association between RVF outbreaks and precipitation, temperature and NDVI. These results

demonstrate that RVF outbreaks in Ijara are significantly associated with precipitation and

NDVI, which represents ecological changes that promote RVF occurrence, e.g. the

development of vector breeding sites. Based on the log likelihood estimates, cumulative

rainfall for a recent period of 3 months was strongly associated with RVF outbreaks than the

other forms of rainfall variable used in the analysis.


27

NDVI was not however included in subsequent regression models because data that were

obtained from SPOT VEGETATION covered a short period (1999 – 2010) compared to those

for RVF outbreaks, precipitation, and temperature (1979 – 2010).

Table ‎2.7. Historical outbreaks of RVF involving Ijara and the other districts in the north-eastern Kenya

Province District Area Year

North Eastern Ijara Habasweni, Masalani

1961, 1962, 1997, 1998, 2006, 2007

Garissa Garissa, Galmagala, Dadaab, Bura Shantabak, Mbalambala, Danyiri, Saka

1961, 1962, 1997, 1998, 2006, 2007

Wajir Wajir, Adadi, Jole, Hadado, Burder, Habaswein

1961, 1962, 1997, 1998, 2006, 2007

Mandera Mandera, Dantu, Kuturo, Didkuro, Gari

1961, 1962, 1997, 1998, 2006, 2007

Table ‎2.8. Results of the univariate analyses used to assess the association between RVF outbreaks in Ijara district and precipitation, temperature and NDVI obtained from ECMWF

Variable Variable formulations Log likelihood Wald test P > |Z|

Precipitation (n = 1536)

Monthly rainfall -136.14 0.00

1 month lag -142.21 0.00

2 month lag -143.79 0.00

2 month’s aggregate -134.29 0.00

3 month’s aggregate -132.84 0.00

Temperature (n=744)

Maximum -77.22 0.79

Minimum -75.33 0.06

NDVI (n = 528)

Maximum -8.98 0.05

Minimum -12.47 0.14

Results of multivariate analyses involving rainfall and temperature as fixed effects and

division as a random effect are presented in Table 3.9. These findings show that temperature

is not a significant predictor for RVF outbreaks (p =0.22 [Model I]). The analysis was therefore

repeated without this variable (Model II) to obtain a parsimonious model that can be used to

guide the development of a dynamical model.


28

Table ‎2.9. Random effects logistic regression models evaluating the association between climate variables (precipitation and temperature) and RVF epizootics in Ijara district, Kenya

Variable Model 1: Precipitation + Temperature Model II: Precipitation only

β SE P>|Z| β SE P>|Z|

Fixed effects

Constant 106.09 91.34 0.25 -5.23 0.35 0.00

Precipitation 0.42 0.13 0.00 0.58 0.10 0.00

Minimum temperature -0.37 0.31 0.22 -

Random effects

Division 4.08e-13 0.26 8.78e-11 0.27

Log likelihood -70.73, n = 740 Log likelihood -132.85, n = 1528

2.3.2 Community-based participatory research survey

2.3.2.1 Livestock species kept and wild animals found in Ijara

In all the sites visited, participants listed cattle, goats, sheep, donkeys and chickens as the

common livestock species kept. Cattle, goats and sheep, in that order, are the most

abundant and highly valued species compared to the donkeys and chickens (Table 3.10).

Participants indicated that they don’t keep camels because they are very susceptible to

trypanosomosis, the most prevalent vector-borne disease in the area. Wild animals that

were identified as being common include buffaloes, warthogs, leopards, cheetahs and a

variety of gazelles.

2.3.2.2 Livestock age structures and relevant baseline risk of mortality

Field exercises used to collate data on livestock age structures and their respective risks of

mortality required a lot of time to complete. This activity therefore involved a smaller

number of villages and focused only on cattle, sheep and goats. Most participants identified

at least 4 livestock age categories for each species; these included:

- cattle: Dalan (0-3 months), Ashirow (4-6 months), Sarar (7-36 months) and

Hauwechi (37 months and older);

- goats: Dalan (0-3 months), Sarar (4-5 months), Asan (6-12 months) and Riya (13

months and older), and,

- sheep: Maqal (0-1 month), Saben (2-3 months), Laah (5-6 months) and Hauwechi (7

months and older).

Minimum and maximum ages for each category, relative population sizes, and age category-

specific risk of mortality were also determined. Younger animals, in general, are perceived

to have a higher risk of mortality than older ones. The participants further indicated that


29

mortality levels were higher during the dry than the wet season. Goats are perceived to

have a lower risk of mortality compared to cattle and sheep (Table 3.11).

Table ‎2.10. Types of livestock species kept and their relative population sizes determined using median percentage scores (with 10th and 90th percentiles)

Livestock species Abundance (median %) a

Cattle 32 (23, 46)

Goats 31.5 (18, 43)

Sheep 22 (15, 31)

Chickens 6 (4, 12)

Donkeys 5 (3, 9) a Medians % represent the middle value when numbers are put in order; this column therefore does not necessarily has to add up to 100%

Table ‎2.11. Median proportions (with 10th and 90th percentiles) representing age category-specific risks of mortalities in selected livestock species, by season

Age category Season Cattle Goats Sheep

1 Wet 0.33 (0.15, 0.44) 0.27 (0.08, 0.50) 0.30 (0.12, 0.55)

Dry 0.50 (0.17, 0.72) 0.55 (0.17, 0.61) 0.50 (0.29, 0.65)

2 Wet 0.33 (0.05, 0.40) 0.02 (0.00, 0.33) 0.23 (0.00, 0.40)

Dry 0.27 (0.10, 0.63) 0.42 (0.00, 0.57) 0.38 (0.29, 0.60)

3 Wet 0.07 (0.04, 0.30) 0.10 (0.05, 0.15) 0.13 (0.07, 0.30)

Dry 0.34 (0.06, 0.55) 0.09 (0.00, 0.27) 0.17 (0.10, 0.33)

4 Wet 0.13 (0.02, 0.23) 0.08 (0.00, 0.14) 0.10 (0.03, 0.20)

Dry 0.28 (0.09, 0.50) 0.08 (0.00, 0.18) 0.14 (0.06, 0.40)

Medians proportions represent the middle value when numbers are put in order; this

column therefore does not necessarily has to add up to 100%

Sales and slaughter

Sheep and goats are more likely to be sold (to raise funds that can be used to meet some of

the domestic needs e.g. school fees, purchase of grains, settlement of debts and fines etc.)

or slaughtered compared to cattle (Table 3.12). Most of the sales occur during the dry than

the wet season, with sheep being sold more often than goats. In general, the proportion of

animals slaughtered is higher during the wet than the dry season.

Reproductive performance

Findings on a range of reproductive indices such as the duration that young animals take to

mature, interval between parturition and subsequent heat, proportion of animals that

require repeated services to conceive, twining and proportion of abortions expected during


30

the wet and the dry season are outlined in Table 3.13. Participants, as expected, indicated

that females mature earlier compared to males and that dry weather conditions delay both

the age at first breeding and the interval between parturition and subsequent heat.

In addition, it is perceived that goats have higher proportions of repeat services and higher

twinning frequencies compared to the other livestock species. Goats are also perceived to

have higher baseline abortion risk relative to cattle and sheep. In all the species, the risk of

abortion is higher during the dry than the wet season.

Livestock movement patterns

Plate 3.2 outlines movement patterns of livestock in Hara sub-location, Ijara district. Similar

maps were developed for all the sub-locations visited. General observations made from the

mapping exercise are:

- Boni forest (located along the Kenya Somalia border), the Tana delta and the banks

of River Tana are used as dry season grazing areas. However, Boni forest is heavily

infested with tsetse flies, therefore pastoralists move to this site when there are no

alternative grazing grounds. Animals are also grazed in conservancies such as the

Ishaqbini during the dry season.

- Because of the high tsetse challenge in the Boni forest, small ruminants (sheep and

goats) are seldom taken there. These animals are often grazed in the peripheries of

the forest or in the Tana Delta. In particular, goats are perceived to be more

susceptible to trypanosomosis and they are less responsive to medication.

- The respondents said that cattle are usually moved out of the wet season grazing

sites much earlier than the small ruminants because they are more sensitive to lack

of pasture than goats and sheep.


31

Table ‎2.12. Median percentages (10th and 90th percentiles) of animals sold or slaughtered in Ijara district by seasons (August 2006 to November 2007)

Livestock species Sales (%) Slaughter (%)

Wet season Dry season Wet season Dry season

Cattle 4.5 (0, 20) 6 (2, 15) 2 (1, 3) 0 (0, 2)

Goats 8 (5, 17.5) 15 (4, 21) 4 (0, 17.5) 5 (0, 10)

Sheep 10 (4, 20) 19 (10, 30) 6 (4, 10) 4 (3, 14)

Table ‎2.13. Reproduction parameters estimated from participatory exercises for the most important livestock species raised in Ijara district

Livestock

species

Age at first breeding in months

(n=22)*

Interval between parturition

and subsequent heat in

months

(n=21)*

Proportion of

repeat

breeders

(n=6)*

Proportion of

animals

giving birth to

twins

(n=15)*

Proportion of pregnancies

that are expected to

terminate prematurely

(abortions)

(n=21)*

Females Males

Wet season Dry season Wet season Dry season Wet season Dry season Wet season Dry season

Cattle 36 (36, 48) 48 (48, 60) 42 (42, 60) 48 (48, 60) 6 (1, 12) 12 (12, 24) 0.1 (0.0, 0.3) 0.0 (0, 0.001) 0.2 (0.0, 0.4) 0.3 (0.2, 0.6)

Goats 7 (12, 30) 12 (18, 24) 6 (24, 30) 30 (12, 36) 3 (2, 5) 6 (3, 12) 0.35 (0.2, 0.6) 0.3 (0.1, 0.5) 0.3 (0.2, 0.5) 0.5 (0.2, 0.6)

Sheep 7.5 (6, 24) 12 (8, 18) 6 (12, 24) 10 (12, 24) 2 (1, 3) 5 (2, 12) 0.0 (0.0, 0.2) 0.1 (0.0, 0.3) 0.1 (0.0, 0.5) 0.3 (0.1, 0.5)

*Number of villages where this information was collected


32

- A small herd mainly comprising lactating cows is often left behind in the homesteads

when livestock are moved to the dry season grazing sites. These herds provide milk

for children, women and the elderly people who remain behind in the homesteads,

- Movement between sites could take a short (about 2 days) or a long period (up to 15

days) depending on whether animals can get water and pasture along the migratory

routes,

Plate ‎2.2. A map indicating migration patterns of livestock in Hara sublocation, Ijara district developed during one of the participatory rural appraisal meetings

To better understand climate thresholds for movement, monthly mean NDVI estimates for

the areas where livestock were grazed in during the period considered for these analyses

(July 2011 and July 2012) were obtained; these are summarised in Tables 3.14 and 3.15. The

overall NDVI mean for the study period was 0.42 (95% CI: 0.38 – 0.46).

At the time when sheep/goats and cattle were being moved out of a grazing site, mean

NDVI values were estimated to be 0.15 (0.08 – 0.22) and 0.27 (0.14 – 0.40), respectively.

These values support observations made by participants that sheep and goats have lower

thresholds for movement compared to cattle.


33

Table ‎2.14. Monthly normalised vegetation indices (NDVI) for the areas used to graze sheep and

goats by four different grazing communities in Ijara district over the period July 2012 to July 2012

Grey shading indicates areas where sheep and goats were in a given month. Negative NDVI values

correspond to water, low positive to slightly negative values correspond to bare soil while values

ranging from 0.3 to 0.8 correspond to dense vegetation.

Table ‎2.15. Monthly normalised vegetation indices (NDVI) for the areas used to graze cattle by four different grazing communities in Ijara district over the period July 2012 to July 2012

Month Boni---Korisa Boni---Haji Mohamed

Boni---Gababa---Kitele Boni---Falema--Bura

July 11

0.22 0.92

0.22 0.92

0.22 0.92 0.92

0.22 0.92 Aug 11

0.22 0.85

0.22 0.90

0.22 0.20 -0.05

0.22 0.86

Sep 11

0.12 0.86

0.12 0.76

0.12 0.05 -0.04

0.12 0.86 Oct 11

0.34 0.13

0.34 0.05

0.34 0.68 0.45

0.34 0.26

Nov 11

0.26 0.42

0.26 0.52

0.26 0.16 0.15

0.26 0.67

Dec 11

0.66 0.26

0.66 0.36

0.66 0.25 0.08

0.66 0.56

Jan 12

0.66 0.01

0.66 0.14

0.66 0.24 0.77

0.66 0.38

Feb 12

0.32 -0.10

0.32 -0.03

0.32 0.81 0.48

0.32 0.08

Mar 12

0.38 0.88

0.38 0.92

0.38 0.48 0.16

0.38 -0.04

Apr 12

0.27 0.84

0.27 0.88

0.27 0.37 0.10

0.27 0.92 May 12

0.08 -0.05

0.08 -0.10

0.08 0.52 0.15

0.08 0.05

Jun 12

0.44 -0.10

0.44 -0.01

0.44 0.73 0.22

0.44 0.23

Jul 12 0.40 0.87 0.40 0.85 0.40 0.56 0.07 0.40 0.01

Grey shading indicates areas where cattle were in a given month. Negative NDVI values correspond

to water, low positive to slightly negative values correspond to bare soil while values ranging from

0.3 to 0.8 correspond to dense vegetation.

Month Warende Goga---Shelu

Plain---Bodhai Abalatiro---Warawesa---

Gababa Bodhai---Shelu

Plain Atheweiyno---

Warawesa

July 11 -0.03 0.92 0.92

0.92 0.92 0.92

0.92 0.92

0.92 0.92

Aug 11 0.92 0.74 0.61

-0.09 0.90 0.20

0.61 0.74

0.81 0.90

Sep 11 0.86 0.74 0.13

0.92 0.76 0.05

0.13 0.74

0.77 0.76

Oct 11 0.24 0.46 0.55

0.34 0.22 0.68

0.55 0.46

0.28 0.22

Nov 11 0.65 0.90 0.11

0.10 0.91 0.16

0.11 0.90

0.05 0.91

Dec 11 0.74 0.46 0.15

0.85 0.80 0.25

0.15 0.46

-0.10 0.80

Jan 12 0.52 0.04 0.12

0.39 0.38 0.24

0.12 0.04

0.46 0.38

Feb 12 0.17 0.92 0.71

0.18 0.11 0.81

0.71 0.92

0.12 0.11

Mar 12 -0.03 0.81 0.42

0.01 -0.04 0.48

0.42 0.81

-0.09 -0.04

Apr 12 -0.05 0.86 0.26

-0.03 0.16 0.37

0.26 0.86

0.92 0.16

May 12 0.12 0.82 0.68

0.05 0.39 0.52

0.68 0.82

-0.10 0.39

Jun 12 0.19 0.71 -0.09

-0.03 0.14 0.73

-0.09 0.71

0.85 0.14

Jul 12 0.02 0.68 0.40 0.92 0.07 0.56 0.40 0.68 0.81 0.07


34

2.3.3 Cross sectional surveys

A total of 300 blood samples were collected from livestock that were being moved out of

the dry season grazing areas towards the end of 2012. Forty nine per cent of these samples

were obtained from goats, 35.7% from sheep and 15 % were obtained from cattle that had

not been vaccinated. At the same time, vectors sampled (using CDC miniature light traps

baited with carbon dioxide) included 2,513 Culex, 33 Anopheles, 9 Mansonia and 7 Aedes

mosquitoes.

These samples are being analysed in the molecular laboratory at ILRI for RVFV infection. In

addition, blood meal sources for mosquitoes are also being investigated using PCR tests.

2.3.4 Preliminary predictions

2.3.4.1 Vector population dynamics and RVFV incidence in livestock

Predicted population dynamics for Aedes app and Culex spp driven by daily TRMM rainfall

are given in Figure 3.4.These populations are used to estimate the force of infection, and

hence the probability of a host getting infected with RVF virus. Predicted RVF virus infection

incidences in cattle and sheep that follow the upsurge in the number of mosquitoes are

presented in Figure 3.5.

Figure ‎2.4. Predicted population levels of Aedes and Culex mosquitoes over the simulation period

The inset graph in Figure 3.5 demonstrates that RVF epidemics tail off slowly depending on

the rate of disappearance of the flood waters. The main graph also indicates that there are

periodic occurrences of RVFV related with


35

Figure ‎2.5. Predicted RVFV incidence in cattle and sheep over the simulation period; the inset graph illustrates the patterns of the epidemic at a relatively higher temporal resolution

The model has also been used to conduct a number of scenario analyses. Results of an

analysis assessing the effect of varying the area under floods (5 – 50%) are presented in

Figure 3.7.

2.3.4.2 Immunity dynamics

Predictions given in Figures 3.4 and 3.5 suggest that even though there was heavy

precipitation, followed by an upsurge in the number of Aedes and Culex mosquitoes

between days 865 to 921, an insignificant outbreak of the disease occurred in livestock at

the time. Predictions given in Figure 3.6 suggest that naturally acquired immunity could

have played a role in limiting the likelihood of a full-blown epidemic. During this period,

peak incidence of the disease in cattle is predicted to have been below 5% since over 60% of

the animals were immune. This immunity declined over time such that by day 2000, 40% of

the animals were immune. Immunity can therefore play a big role in dampening RVF

outbreaks as well as in determining their frequency of occurrence. These analyses are being

refined so as to help in determining the duration of herd immunity acquired following RVFV

outbreaks.


36

Figure ‎2.6. Predicted changes in immunity levels following natural exposure to RVFV in cattle

2.3.4.3 Varying the number of mosquito breeding sites

Increasing the number of the mosquito breeding sites increases the populations of vectors,

hence the force of RVFV infection, and the probability of an animal encountering at least

once mosquito breeding site as it moves around while grazing. Predictions given in Figure

3.7 demonstrate that higher numbers of mosquito breeding sites produces higher incidence

of RVFV in cattle that also develops much faster than lower number of breeding sites.

Figure ‎2.7. Expected effect of varying the number of Aedes spp and joint Aedes and Culex spp breeding sites on RVFV incidence in cattle


37

2.3.4.4 Validation

The model mimics temporal patterns of the recent 2006/2007 RVF epidemics – Kenya

officially reported the epidemic in December 2006 while Tanzania reported (as stated

earlier) in February, 2007. However, the time interval between the provisions of official

reports between the two countries was longer than the predicted interval of occurrence.

More analyses will be done to determine the cause of this discrepancy which might be

associated with:

- Differences in response times, hence provision of reports by country

- Failure of the model to respond to precipitation changes

- Inaccuracies with precipitation measurements

Figure ‎2.8. Predicted RVFV incidence in Ijara, Kenya and Arusha Tanzania

2.4 Discussion

RVF transmission dynamics are influenced by multiple drivers that act at various time and

spatial scales. Tremendous progress has been made in identifying some of the main drivers

and transmission processes, particularly the relationships between RVF outbreaks and

physical or climatic factors. Anyamba et al. (2009), for instance, have developed a prediction

system based on climate anomalies that can be used for predicting outbreaks with 2 to 6

week lead time. The present work recognises the need to employ a multidisciplinary

approach to generate more knowledge on the disease transmission mechanisms. It

therefore uses an individual-based RVFV transmission model as an analytical framework for

generating hypotheses for further work. The model simulates interactions between the

various components of the disease system including vectors, hosts, and the environment

and its processes are driven by climatic and socio-economic variables. This approach

therefore represents an initial attempt to study how climate drivers interact with


38

local/socio-economic processes such as livestock movements, off-take rates and herd

immunity changes to influence the incidence of RVF.

The analysis of historical data shows that RVF outbreaks are associated with excessive and

persistent rainfall that lasts for a period of at least 3 months. It also reveals that

temperature variability is not a significant predictor although these findings will be verified

as the statistical model is refined, for example, through the inclusion of other districts and

key predictors that could not be used. Nonetheless, similar results have been reported by

Anyamba et al. (2012) and they are consistent with observations made by Logan et al.

(1991) and Linthicum et al. (1983) that flooding for 10-15 days is necessary for the

emergence of RVFV infected Aedes floodwater breeding mosquitoes and that the

persistence of floodwaters for a further 4-6 weeks and their colonization by secondary

mosquito vectors allows for the amplification of the virus to epidemic proportions. Anyamba

et al. (2009) also indicates that RVF outbreaks occur after excessive rainfall and flooding,

often associated with El Nino weather phenomenon in the Horn of Africa. El Nino weather

patterns follow an anomalous warming of the sea surface temperatures (SSTs) by >1 ⁰C in

the eastern-central pacific region and concurrent anomalous warming of SSTs (>0.5 ⁰C) in

the western equatorial Indian Ocean leading to increased precipitation (Anyamba et al.,

2009). They indicate that in 2006/2007, cases of RVF occurred after 3-4 months of sustained

above normal rainfall and associated green-up in vegetation. These observations have been

used in setting thresholds for the RVFV transmission model though more work is needed to

refine hydrological dynamics that lead to flooding. Analyses on historical data have utilised

animal and not human outbreak data although both human and livestock cases were

reported in the district during the 1997-98 and 2006-07 outbreaks. Attempts are being

made to collate human cases and identify risk factors involved in anima-human transmission

so as to estimate the expected impacts of the disease (on both human and livestock health

and livestock trade).

Animal movements contribute immensely to the transmission and maintenance of

infectious diseases. For the purposes of this work, animal movements are classified into

three levels depending on the range of distances covered; these are:

(i) International/trade-related movements. For instance RVFV is believed to have been

introduced into the Middle East by ruminants transported from Kenya that had

experienced the outbreak in 1997/1998 (Abdo-Salem et al. 2011),

(ii) seasonal migrations across ecological zones associated with pastoralism, and

(iii) local movements within settlement areas.


39

This report focuses on the second and third levels of movement since these could be

relevant for RVFV transmission in Ijara. The local Somali community practice transhumant

pastoralism (involving seasonal migration patterns) as the key socio-economic activity to

cope or manage the effects of adverse climate. Animals are moved from inhabited areas

with diminishing pasture and water to areas where these resources can still be found.

Participatory survey established that the number of movements undertaken in a year

depends on environmental conditions and the type of animals kept. An analysis of these

movements against NDVI estimates as a proxy for climate variability indicates that there is a

pattern of increased movement during periods of low NDVI. Small ruminants have a higher

NDVI threshold for movement than cattle since they browse on a variety of shrubs that can

withstand drought conditions for a slightly longer time than the normal pasture. Similar

analyses have been used previously by Worden (2007) to analyse livestock movement

dynamics in the greater Amboselli ecosystem in Kenya. Low NDVI estimates, however, might

not always imply increased livestock movement because they measure the amount of

greenness or green forage that is present in an area rather than pasture availability. In fact

drought mitigation strategies focus more on accessing standing dry biomass rather than

green forage. Nevertheless, these estimates can be valuable for guiding livestock movement

dynamics in the model. It can also be correlated with rainfall density, as it has been done in

agronomy, to allow for predictions of future movement patterns assuming that there are

minimal changes in land use patterns.

Efforts are underway to determine whether seasonal/transhumant migrations influence RVF

transmission/persistence. Areas used as dry season grazing sites e.g. the Boni forest and

riverine vegetation along River Tana have the potential to sustain an endemic transmission

of the virus since they have a rich diversity and density of animals and vectors. Observations

made by Shope et al. (1982) indicating that the virus can exists in endemic cycle in forests or

in humid and shrubby grasslands are very relevant in this case. Analysis of the biological

samples collected from animals that were being brought back from these areas would

therefore be invaluable for this assessment. If it is established that these areas have some

RVFV activity, then it is likely that exposures that occur while livestock are being grazed

there help in sustaining naturally acquired immunity. These hypotheses are consistent with

unproven opinions suggesting that major RVF outbreaks occur after prolonged periods of

drought when a large proportion of otherwise immune animals are lost, and so they get

replaced with naive populations.

It has been shown that local livestock movements amplify the rate transmission of an

infectious disease especially if movements occur in the course of an outbreak. Anyamba et

al. (2010) observe that movement of vireamic animals to other ecological zones in the

course of RVF outbreaks amplifies outbreaks especially if these areas have large populations

of Culex mosquitoes that play a role in creating secondary RVF transmission foci. Scenario

analyses conducted using the RVFV model (not shown) suggest that the range of distances


40

covered per day correlate positively with size (incidence and duration) of an epidemic. This

is due to the fact that there is an increased chance of an animal getting into a vector

breeding zone the further it moves away from its base.

Outputs from the transmission model suggest that herd/flock immunity against RVF can

influence the size and intervals of the outbreaks. This appears to be more important in

cattle, given their lower turn-over rates, than sheep. It is currently thought that an animal

that recovers from natural infection remains immune for the rest of its life. This implies that

livestock offtake rates (sales, slaughters and mortalities) are very important in determining

the longevity of acquired herd-level immunity by influencing the rates at which immunized

animals are removed from the herds/flocks. Preliminary findings show that small ruminants

have high turn-over rates compared to cattle. During the dry season for instance, 19% of

sheep and 15% of goats are sold to meet some of the household needs. The high offtake

rates negatively affect the persistence of herd immunity. The data collected from these

surveys will be analysed further and used for the prediction of immunity dynamics over

time.

There are many other factors that can influence RVFV transmission dynamics which cannot

be exhaustively addressed by this report. One of this is the type of hosts that are present in

an area (a measure of biodiversity). Participatory surveys identified types of livestock

species being kept in the area, their relative population sizes as well as wildlife species that

are common in the district. This information is being used to determine types of hosts that

should be considered when developing a multi-host model. It is known however that there

is a huge variability in the susceptibility of the various animal species to RVFV infection.

Domestic animals, for instance, have been listed in a decreasing order of susceptibility as:

sheep, goats, cattle, camels and water buffaloes (FAO, 2003). Similarly, antelopes, cape

buffaloes, monkeys, cats, dogs and rodents are known to be susceptible while birds, reptiles

and amphibians are refractory to RVFV infection. The presence of such a big diversity of

hosts in an area can either promote the transmission of the disease (e.g. by providing a

larger potential source of blood meal for the vectors or harbouring the virus, etc ) or reduce

further pathogen transmission especially is some of them act as dead-end hosts. This is one

of the interesting aspect of the topics that would be addressed as the model is expanded

and refined.


41

3 Malaria

3.1 Background

Malaria is a major public health problem in Kenya and it accounts for 30% of outpatient

consultations, 15% hospital admissions, and 3-5% inpatient deaths (Njuguna et al., 2012). In

arid and semi-arid areas (e.g. Ijara district), malaria transmission is extremely seasonal since

the vectors that are involved (mainly Anopheles arabiensis and An. gambiensis) are sensitive

to climate variability. These vectors are confronted with highly variable and challenging

climatic conditions, particularly during the dry seasons, which cause drastic shrinking or

complete disappearance of larval habitats, a decline in the vector population and hence a

reduction in the incidence of the disease. Build-up of a new population of vectors in

subsequent wet seasons arise either from new populations of immigrants from the

neighbouring areas or an expansion of the small local populations that survive the dry

period (Mala et al., 2011). Given that Anopheles eggs have low tolerance to desiccation,

adults have to survive the dry spell in order for the species to survive by hiding in barrows,

abandoned houses, etc.

Malaria cases often cluster by geographic/ecological, socio-economic, or demographic

factors. In Arid and semi-arid areas, closeness to a river, watering points or irrigated areas

has been associated with an increased prevalence of the disease (Oesterholt et al, 2006).

Other risk factors that have been reported include living in grass-thatched houses (preferred

by mosquitoes), engaging in outdoor occupations such as herding cattle, low altitude, and

dense vegetation cover (Mala et al., 2011; Noor et al., 2009). These relationships are,

however, not linear; Ijumba and Lindsay (2001) indicate that the use of vector control

measures such as bed nets or improved access to medical services masks the expected

effects of these risk factors. In fact recent observations indicate that malaria caused by P.

falciparum is declining in sub-Saharan Africa due to large-scale bed net programmes and

improved case management. Malaria risk mapping work done by Noor et al. (2009) also

shows that a large proportion of Kenya (94%) has low intensity transmission which can be

difficult or costly to quantify empirically.

The intensity of malaria transmission is often measured using: (i) the entomological

inoculation rate (EIR), which represents the average number of infective bites per person

per unit time, and (ii) Ro, the average number of secondary infections in a non-immune

population resulting from a single new infection. However, both of these indices are difficult

to measure directly. EIR, for instance, is estimated as the product between the proportion of

mosquitoes carrying sporozoites in their salivary glands (sporozoite-rate) and the mosquito-

human biting rate. In semi-arid areas (like Ijara district), sporozoite rate is usually very low

and seasonal. Mosquito-human biting rate is also influenced by many factors such as the

density of the mosquitoes, relative locations of mosquito breeding sites and areas of human


42

aggregation. Alternative measures for P. falciparum risk have been developed and used

since the 1950s, e.g. parasite rate (PfRT), which represents a proportion of a random sample

of population with malaria parasites in their peripheral blood, spleen rates, etc. PfRT has

been used to map malaria risk in Africa.

This is a preliminary analysis that uses hospital records obtained from health facilities in

Ijara district to determine whether the number of cases reported in the district can be

associated with climate variables – precipitation and temperature. The data represents the

number of outpatient malaria cases recorded over a 5 year period and the proportion of the

cases that are found to be positive for malaria following laboratory investigation. This

analysis is however prejudiced by the fact that hospital records do not necessarily represent

the background incidence of a disease. In this case, more work will be done to estimate EIRs

and repeat the analysis in order to generate more solid evidence on the linkages between

climate and malaria transmission.

3.2 Methodology Hospital records on malaria cases in Ijara district for the period 2006 to 2011 were obtained

from the District Health Records and Information Office. The data comprise monthly records

of inpatient and outpatient cases; mortalities from the inpatient cases; the number of cases

tested versus those that turned positive for malaria following laboratory investigation; and

annual quantities of insecticide-treated nets and long lasting insecticide treated nets,

artemisinin-combination therapy distributed to people and the number of houses covered

with indoor residual spraying.

Descriptive analyses were done to explore trends in malaria incidence based on the number

of outpatient cases and the proportion that turned positive on laboratory investigation.

Subsequently, simple statistical analyses using Generalised Linear Model (GLM) were done

to assess the correlation between these outcomes (total number of outpatient cases and

proportion of the cases that turned positive) and climate variables: mean precipitation,

mean minimum and maximum temperature estimates for the district obtained from

ECMWF. Both current and lagged (1 and 2 months) rainfall and temperature estimates were

used in the analysis. The dependent variables iy were assumed to have a normal

distribution with mean i and variance 2 represented as: ),( 2ii Ny , and the general

structure of the model:

iii exy 110 (Eq. 7)

The two climate variables (precipitation and temperature) were used in the analysis because

they have been shown to influence the incidence of malaria. Precipitation influences

humidity and causes the development of mosquito larva habitats. Changes in temperature


43

0

200

400

600

800

1000

1200

1400

Jan

May

Sep

Jan

May

Sep

Jan

May

Sep

Jan

May

Sep

Jan

May

Sep

Jan

May

Sep

All cases

Cases under 5 years

Nu

mb

er o

f ca

ses

Month

2006 2007 2008 2009 2010 2011

and humidity affect vector distribution and development, survival, susceptibility to

pathogens, pathogens’ replication rates in the vector and their extrinsic incubation periods.

3.3 Results

3.3.1 Descriptive analyses

The total numbers of outpatient and inpatient malaria cases reported over the period (2006

– 2011) were 33,618 and 1,308, with those under 5 years of age representing 47% and

41.3%, respectively. Figure 4.1 shows the distribution of these cases by month. It also shows

that there was a steady increase in the number of cases until mid – 2009 when it started

declining.

Figure ‎3.1. Trends in the total number of outpatient malaria cases and those for patients less than 5 years old attended to in all the health facilities in Ijara district in 2006 to 2011.

Figure 4.2 shows the trend in the proportion of cases, out of those tested, that were

confirmed following laboratory analysis. A majority of the cases were found to be negative

for malaria; 83% of the cases had less than 40% probability of being diagnosed as positive.


44

0.00

0.10

0.20

0.30

0.40

0.50

0.60

Jan

May

Sep

Jan

May

Sep

Jan

May

Sep

Jan

May

Sep

Jan

May

Sep

Jan

May

Sep

Month

2006 2007 2008 2009 2010 2011

Prop

orti

on o

f cas

es t

hat

are

posi

tive

for

mal

aria

Figure ‎3.2. Monthly trends in proportion of cases that are positively diagnosed for malaria using laboratory tests in Ijara district over the period 2006 – 2011

Interventions implemented by the public health department in response to these cases are

outlined in Table 4.1. More emphasis was placed on the use of indoor residual spraying in

2007, which also targeted a bigger population, than in the subsequent years. A lot of

arteminisin-combination therapies were used in 2008.

Table ‎3.1. Annual statistics on the numbers of insecticide treated nets (ITNs), long lasting insecticide treated nets (LLINs), arteminisin-combination therapies (ACTs) and the number of houses covered with indoor residual spraying (IRS) obtained from Ijara district for the period 2006 - 2007

Year ITN LLINs ACTs IRS

No houses sprayed No. of houses targeted No. of people

protected

2007 372 10,478 3,041 6,880 8,840 20,640

2008 200 0 20,703 1,524* 3,824 4,572

2009 0 0 3,083 3,098 5,480 9,294

2010 0 782 3,019 80* 2,128 240

2011 0 24,000 1,659 10,813 11,133 32,439

* Low numbers of houses were covered with IRS in the years 2008 and 2010 because the Ministry of Health did not receive external support at the time. This implies that only local government resources were used to fund the intervention. The Mentor Initiative (http://thementorinitiative.org/) is now partnering with the local government to improve the effectiveness of malaria response measures.

http://thementorinitiative.org/


45

3.3.2 Results of statistical analyses

Outputs from the statistical analyses conducted using the GLM model show that both the number of reported malaria cases and the proportion of positive cases obtained from laboratory investigation are not correlated with either precipitation or temperature (Table 4.2).

Table ‎3.2. Results of statistical analyses conducted to investigate the correlation between climate variables (rainfall and temperature) and hospital records of malaria cases and the proportion of positive cases obtained from laboratory analyses (Ijara district, 2006 – 2011)

Variable Formulation Total number of cases Proportion of positive cases

β (SE) P<|Z| Log likelihood

β (SE) P<|Z| Log likelihood

Precipitation

Monthly rainfall

-0.56 (0.53)

0.29 -408.30 -0.11 (0.42)

0.79 -393.93

1 month lag -0.10 (0.54)

0.86 -408.96 0.33 (0.42)

0.43 -393.37

2 month lag 0.07 (0.53)

0.90 -407.76 -0.27 (0.42)

0.51 -393.69

Maximum temperature

Monthly maximum

-1.19 (21.95)

0.96 -329.50 -3.45 (16.41)

0.83 -315.58

1 month lag 16.28 (21.62)

0.45 -328.77 5.97 (16.27)

0.71 -315.20

2 month lag 7.61 (21.05)

0.72 -327.51 26.57 (15.83)

0.10 -313.88

Minimum temperature

Monthly minimum

-4.92 (38.78)

0.90 -329.50 31.56 (28.52)

0.27 -314.97

1 month lag 10.97 (38.40)

0.77 -329.03 46.37 (27.87)

0.10 -313.86

2 month lag 41.72 (36.75)

0.26 -326.92 52.98 (27.98)

0.06 -313.47

3.4 Discussion

This analysis explores unconditional relationship between the incidence of malaria and

climate (rainfall and temperature) in Ijara district based on cases obtained from the health

facilities in the district. This is a simple analysis which is done while recognising the fact that

other biological and non-climatic factors are equally important in the disease epidemiology.

The records used in the analysis are aggregated by facility/month; this might help to reduce

noise in the data. The catchment areas for the health facilities are also quite large relative to

population densities. The district has a total of 11 heath facilities comprising one district

hospital that serves a population of 100,000 people, one sub-district hospital and three

health centres, each serving a population of 30,000 people and six dispensaries, each

serving a population of 10,000 people (Njuguna et al., 2012). The representativeness of the


46

data can also be questioned considering the fact that a large proportion of the target

population seek medical services from private clinics, pharmacies and traditional healers,

etc. which are not captured by the public health surveillance. Nevertheless, the quality of

surveillance for infectious diseases has been improving in the country following the

introduction of Integrated Disease Surveillance and Response (IDSR) program by WHO and

CDC.

Most studies have demonstrated that climate factors are important drivers for malaria

transmission, affecting both the development rates of the malaria parasites and vectors.

This topic has generated a lot of interest because of the expected impacts of climate change

on human health. A rise in temperature is expected to increase the transmission and

prevalence of malaria by increasing the vector feeding rate and by shortening the incubation

period of the parasite in the vector. Precipitation, on the other hand, provides a medium for

the development of the aquatic stages of the vector and increases humidity, which

enhances the longevity of the vector (Alemu et al., 2011). A recent analysis by Akinbobola

and Omotosho et al. (2012), for instance, reported that rainfall (with a lag of one month)

and maximum temperature are positively correlated with malaria incidence in Nigeria.

Contrary to the expectations expressed above, this study did not find any correlation

between climate variables and incidence of malaria in Ijara district. This can be attributed to

increased uptake of malaria prevention and control measures such as IRS, ACTs and LLINs.

Njuguna et al. (2012) reports that a majority (76.5%) of the cases reported in these facilities

are diagnosed using clinical examinations and no laboratory confirmations are done. In fact

the degree of positivity that is obtained following laboratory diagnosis rarely goes beyond

40%. This trend is thought to cause an over-representation of malaria incidence and hence

an over-treatment. In fact it has been demonstrated that spleen and parasite prevalence in

communities that live in villages with health facilities are significantly lower than those

communities that live in villages without these facilities (Mboera et al., 2008).

There is a need for more studies on the relationship between climate variability and malaria

transmission dynamics, and how it is influenced by anthropogenic drivers, including the

application of large scale intervention measures. It has been reported that the endemicity

and geographical extent of the disease is declining globally, and yet there are predictions

that suggest an increased burden of the disease as a result of the global climate change

(Gething et al., 2010). This paper also observes that non-climatic factors such as disease

control, indirect effects of urbanization and economic development have had greater

influence on the geographic extent and intensity of malaria worldwide than have climatic

factors.

4 Way forward on RVF work

More work is being done to refine the RVFV model particularly on developing an

appropriate module to simulate flooding dynamics. This needs to be driven by topography,


47

soil types, precipitation and temperature. There is also a need to develop a way of

incorporating wild life, for example having a group of hosts that have variable contribution

to the RVFV transmission. In addition, the model does not explicitly include people yet it

would be necessary to determine the impact of the disease on humans. This has not been

one because it is believed that infections that have substantial impacts are acquired through

contact with tissues and (or) fluids of infected animals. This will not be possible to model

dynamically. However, a parallel survey is being conducted to identify the proportion of

people that engage in risk practices such as slaughtering animals, consumption of uncooked

meat etc. to be used for the development of a statistical model that estimates the risk of the

disease in humans when there are outbreaks in animals.

Biological samples that have been collected so far are inadequate. More sampling will be

done particularly in the dry season grazing areas to determine whether they support an

endemic transmission of RVF.

Finally, RVF is a zoonotic disease and there is a need to collect socio-economic data that can

be used to assess factors that promote exposure to humans. This work will be done in

collaboration with the University of Nairobi.

Acknowledgements

We received tremendous support from a large number of people. Andrew Githeko helped

us so much in the collation of malaria records from the local hospitals in Ijara district. RVF

records were obtained from CDC Kenya. We also thank all the participants of the focus

group discussions we held in Ijara. This report was reviewed by Mark Booth and Felipe de

Jesús Colón-González.


48

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d3.2 rvf/malaria study site analysis

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